Artificial cognitive architecture incorporating cognitive computation, inductive bias and multi-memory systems

Abstract

A computer-implemented method for continual task learning in an artificial cognitive architecture that includes a first neural network module for encoding explicit knowledge representations, a second neural network module for encoding implicit knowledge representations, and a memory buffer. A visual data stream is provided to the architecture. Visual data samples are stored from said visual data stream in the memory buffer. Both visual data samples of the visual data stream and visual data samples from the memory buffer are processed using the first neural network module for learning explicit knowledge representations. Both samples of said visual data stream and visual data samples from the memory buffer are processed using the second neural network module for learning implicit knowledge representations. Information is transformed and shared between the first neural network module and the second neural network module, such as learned knowledge representations, stored within the second neural network module into the first neural network module, as well as transforming and sharing information between sub-modules of the second neural network.

What is claimed is: 
     
         1 . A computer-implemented method for continual task learning in an artificial cognitive architecture comprising:
 a first neural network module for encoding explicit knowledge representations,   a second neural network module for encoding implicit knowledge representations itself comprising a plurality of neural network sub-modules for mutually different implicit functions, and   a memory buffer,   the method comprising the steps of:
 providing a visual data stream to the architecture; 
 storing visual data samples from said visual data stream in the memory buffer, 
 processing both visual data samples of the visual data stream and visual data samples from the memory buffer using the first neural network module for learning explicit knowledge representations; 
 processing both samples of said visual data stream and visual data samples from the memory buffer using the second neural network module for learning implicit knowledge representations; 
 transforming and sharing information between the first neural network module and the second neural network module, as well as transforming and sharing information between sub-modules of the second neural network. 
   
     
     
         2 . The method according to  claim 1 , wherein the plurality of neural network sub-modules comprise a first and second sub-module wherein the method comprises the steps of:
 consolidating knowledge from the first neural network module in a first sub-module of the plurality of neural network sub-modules of the second module as an implicit memory; and   processing an implicit inductive bias using both the visual data stream and visual data samples from the memory buffer in a second sub-module of the plurality of neural network sub-modules of the second module.   
     
     
         3 . The method according to  claim 2 , wherein the plurality of neural network sub-modules comprises a third sub-module, and wherein the method comprises the step of:
 consolidating information from the second sub-module within the third sub-module.   
     
     
         4 . The method according to  claim 3 , wherein the third sub-module acts as a regularizer. 
     
     
         5 . The method according to  claim 2 , wherein the step of transforming and sharing learned knowledge representations from the second neural network module into the first neural network module comprises sharing information from the first, second and third sub-modules into the first neural network module. 
     
     
         6 . The method according to  claim 2 , wherein the step of consolidating knowledge from the first neural network module in a first sub-module occurs at a regular interval, and wherein information from a consolidated learning of first sub-module is transferred to both the first module and second sub-module. 
     
     
         7 . The method according to  claim 2 , wherein the first module and second sub-module learn on their own modality with a supervised cross entropy loss on both the samples of the visual data stream and the samples of the memory buffer. 
     
     
         8 . The method according to  claim 1 , wherein the memory buffer is continuously or intermittently supplemented with new samples from the visual data stream replacing already present samples within said memory buffer, and wherein the method comprises:
 applying a logit loss between present samples and new samples.   
     
     
         9 . The method according to  claim 1 , wherein the step of sharing information between the first and second module is governed by a knowledge sharing loss objective. 
     
     
         10 . The method according to  claim 9 , wherein a minimum Mean Squared Error is employed as the objective for all the knowledge sharing losses. 
     
     
         11 . The method according to  claim 1 , further comprising the step of:
 using the second neural network module for decision making based on the visual data stream.   
     
     
         12 . A computer program product comprising instructions which, when the program is executed by a computer, causes the computer to carry out the method of  claim 1 . 
     
     
         13 . An at least partially autonomous driving system comprising:
 at least one camera designed for providing a visual data stream for visual data samples, and   a computer designed for classifying and/or detecting objects using:
 i) the artificial cognitive architecture according to  claim 1 , wherein the cognitive architecture continues to train the first neural network module, and 
 ii) the second neural network module, wherein said second neural network has been trained together with the first module using the method according to  claim 1 . 
   
     
     
         14 . The method of  claim 2  wherein the step of consolidating knowledge from the first neural network module in a first sub-module of the plurality of neural network sub-modules of the second module as an implicit memory is performed through stochastic momentum updates. 
     
     
         15 . The method of  claim 3  wherein the step of consolidating information from the second sub-module within the third sub-module is performed through stochastic momentum updates.